Sandip Chanda

Alleviation of line congestion using Multiobjective Particle Swarm Optimization

This paper presents a methodology based on Particle Swarm Optimization technique for rescheduling of generation patterns to manage congestion in contingent power networks. In deregulated systems, line congestion attract additional penalties which add on to the overall operational cost to be incurred by the Independent System Operators (ISO), apart from causing limit violations, and stability problems. Thus, limiting the congestion level of lines and restricting power flows within the safe limits is important from stability as well as economy point of view. The algorithm proposed in the present paper uses a Standard Sensitivity Index to identify the congested zone(s) in a large power network and then adopt corrective actions for limiting line congestion at the cost of a nominal rescheduling cost without any load curtailment and installation of FACTS devices. It has been demonstrated that the proposed method can reduce congestion even below the minimum level obtained from the conventional cost optimization results. It has been depicted that the methodology on application can provide better operating conditions in respect of improvement of bus voltage profile. The efficiency of the proposed methodology has been tested on a IEEE 30 bus benchmark system and the results look promising.

An intelligent energy management system to optimise demand response in Smart Micro Grids

This paper proposes the development of an intelligent Energy Management System (EMS) at consumer end to utilize the sensitivities of the local renewable generation, battery backup and Plug In Electric Vehicle resources to the changes in grid power input and price of electricity for utility and Demand Response optimization in Smart Micro Grids. In Demand Response usually the price elasticity of demand is considered as per statistical data. In this work the development of an optimized Demand Response(DR) has been depicted for reliable and efficient operation of Micro grids with Smart Communication facility. A housing complex, in the proposed work has been viewed as a future micro-grid and it has been demonstrated that the EMS developed can be effectively programmed to minimize the consumer payment by efficient utility and load management with the assistance of pricing signals from the Smart Grid. The choice of Particle Swarm Optimization(PSO) was compelling for the nonlinear nature of the optimization surface. The results obtained from simulation looked promising from context of future Smart Micro Grids.

A multi-objective optimisation algorithm with swarm intelligence for contingency surveillance

This paper proposes a new multi-objective optimization model to minimize congestion cost, load curtailment and generation cost simultaneously to restore the equilibrium of operating point of the system under contingency. The solution algorithms of the proposed method are based on the Particle Swarm Optimization (PSO) in which load curtailment and generation cost have been optimized without breaching line flow constraints for congestion management. The significance of the proposed method has been presented in this paper by a comparative study with the conventional cost optimization method in terms of operating cost considering VOLL (Value of lost load) and two more proposed analytical indices namely Value of Congestion Cost (VOCC) and Value of Excess Loss (VOEL) in contingent states of power system. It has been depicted that the proposed method effectively reduces the operating cost volatility in spot power market with respect to the conventional methods. The applicability of the developed method has been tested on the IEEE 30 bus system.

Social welfare maximisation with a state space based model and methodology

The state space based model developed in this paper maximizes load catering and simultaneously minimizes the operating standard constrained generation cost for the redemption of power market equilibrium in the most inadvertent states of the network endowed with smart grid communication technology. For optimum utilization of smart metering facility, the model effectively involves resources like demand response, generation surplus to optimize the market clearing price as well as profit of the market participants by effective categorization. A novel curtailment strategy has also been proposed to amalgam stability restoring shedding with profit retentive load cut. The model has been tested in IEEE 30 bus system with Particle Swarm Intelligence based optimization methodology in comparison with standard curtailment based optimization technique to produce encouraging results.

Small signal stability optimization technique of real time OPF model for modern power networks

A suitable optimization model for modern power networks has been proposed in this paper, to depict the possibility of optimum utilization of the available resources, for obtaining a stable operational zone of the grid, satisfying all the indispensible factors. The state space based model developed along with the proposed small signal stability constraint methodology maximizes load catering at minimum generation cost to restore power market equilibrium even in the most inadvertent states of the network. Modern power networks have been observed to operate in stressed conditions as the producers and consumers of power continuously bargain and alter their stand in power market. The perturbation of price of electricity is thus inevitable with a contingency, as the Independent System Operator endeavors to sustain the other operational constraints viz frequency, voltage profile within specified limit. This dynamics of price has to be reconciled in order to ensure the system operation without harmful bifurcation under these small signal disturbances. The restoration of the power market dynamic price equilibrium has been negotiated in this paper, by forming the sensitivity matrix to regulate the state variables for the standardization of the quality of solution in worst possible contingencies of the network. The model has been tested in IEEE 30 bus system and illustrious Particle Swarm Optimization has assisted the fusion of the proposed model and methodology.

Application of particle swarm optimisation for relieving congestion in deregulated power system

This paper presents a Swarm intelligence based Optimization technique to manage congestion in power system networks with transmission line overload. To maintain all the power transaction requests, line congestion in deregulated systems is almost inevitable which may degrade the system stability, security and reliability by additional line outages. In practice System Operators (SO) charge additional price known as congestion management charge against line limit violation. Thus, restricting power flows within the safe limits is important from stability as well as economy point of view. The algorithm proposed in the present paper uses a standard congestion sensitivity Index to identify the congested lines in a large power network and optimizes ‘congestion management charge’ without any load curtailment and installation of FACTS devices. The operating conditions with the proposed methodology have been demonstrated to be subjected to an improvement with reference to conventional method. The applicability of the proposed methodology has been presented on IEEE 30 bus benchmark system.

An evolutionary algorithm for consumer welfare optimisation of a contingent power network

An evolutionary optimization technique based methodology has been proposed in this paper to sustain the total generation cost even in contingent states of a power network for consumer welfare. The alteration of generation cost during contingency is quite evident which makes the consumers suffer economically due to rise of the level of congestion and price of electricity. The aim of this proposed methodology is to minimize the deviations of generation cost, during contingency, from a preferred value by re-allocation of generation schedule with a controlled load curtailment technique and hence relieving the lines from overloading for congestion management. It has been demonstrated that, on application, the proposed methodology can improve the operating conditions of the system apart from optimizing the price volatility of electrical power market. The methodology has been tested on a standard benchmark system and the comprehensive simulation results looked promising.

Impacts of FACTS and HVDC interconnecting link in a deregulated power system with contingency and heavily loaded condition

This paper focuses on the effective utilization of Flexible AC Transmission System (FACTS) and High Voltage Direct Current (HVDC) interconnection link for the improvement of voltage profile, stability and loss allocation in heavily loaded as well as contingent conditions of the network. The weakest bus interconnection has been replaced by FACTS and HVDC link and a study has been carried out to prove their effectiveness in an interconnecting network under deregulated environment. The IEEE 14 bus system, operating with common loads and generators is used to demonstrate the usefulness of FACTS and HVDC link. A new voltage stability index (VSI) has been proposed in this paper for stability analysis with HVDC and FACTS. A comparative analysis between FACTS and HVDC has also been presented in this paper, for their comparison, in field of power quality and cost efficiency.